On the learning of rule uncertainties and their integration into probabilistic knowledge bases

Abstract

We present a natural and realistic knowledge acquisition and processing scenario. In the first phase a domain expert identifies deduction rules where he thinks that they are good indicators of a specific target concept to occur. Then, in a second knowledge acquisition phase, a learning algorithm automatically adjusts, corrects and optimizes the deterministic rule hypothesis given by the domain expert by selecting an appropriate subset of the rule hypothesis and by attaching uncertainties to them. Finally, in the running phase of the knowledge base we can arbitrarily combine the learned uncertainties of the rules with uncertain factual information.